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Record W7032999631

OMVPE growth and characterization of carbon doped InAs

2008· dissertation· en· W7032999631 on OpenAlexfundno aff

Bibliographic record

VenueSummit (Simon Fraser University) · 2008
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicSemiconductor Quantum Structures and Devices
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaSimon Fraser University
KeywordsRaman spectroscopyAnnealing (glass)Analytical Chemistry (journal)ConductivityEpitaxyDopingCarbon fibersHydrogen
DOInot available

Abstract

fetched live from OpenAlex

Carbon-doped InAs samples grown by organometallic vapor phase epitaxy were studied by Raman and IR spectroscopy. Local vibrational modes (LVMs) related to isolated substitutional carbon acceptors, carbon acceptor-hydrogen complexes, and dicarbon centers were detected in samples doped with two isotopes of carbon. Energies of the observed carbon-hydrogen modes are in close agreement with carbon acceptor-hydrogen modes in GaAs and InP, and are consistent with hydrogen occupying a bond-center position in the complex. No sign of substitutional carbon donors was observed. N-type conductivity of carbon-doped InAs was explained by the presence of dicarbon centers that are believed to be deep donors. The stretch mode of this complex was detected at 1832 cm-1 by Raman spectroscopy in as-grown and annealed samples. Annealing measurements performed on heavily carbon-doped InAs samples confirmed that the n-type conductivity observed for this material is due to the formation of dicarbon defects. The structural, electrical, and optical properties of n-type carbon-doped InAs samples annealed at temperatures of 400°C and higher were studied using Raman and infrared spectroscopy, and X-ray diffraction. Based on the expected energy level of dicarbon donors in GaAs, we predicted that the di- carbon defects in InAs should introduce a resonant level close to or slightly above the conduction band minimum.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.740
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.201
Teacher spread0.193 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2008
Admission routes1
Has abstractyes

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